Freeze-drying provides benefits in preserving product quality through minimizing thermal degradation. However, it is limited by extended processing time and high energy use. Recent advancements in artificial intelligence (AI) offer data-driven methods to improve efficiency, control, and product consistency during freeze-drying processes. This review highlights current progress in AI applications across four main areas: process modeling and optimization, real-time monitoring and control, quality control and defect detection, and stability prediction. Machine learning models have been applied to predict drying kinetics, and the combination of computer vision with deep learning has enhanced the precision of product classification. Nevertheless, various challenges remain. These include the limited availability of high-quality datasets, difficulties in model transferability across diverse systems, integrating multiple sensor data, and high computational requirements. Emerging research includes AI-assisted microstructure analysis, AI-enhanced electronic nose and electric tongue systems, AI-based predictions of nutritional quality, and reinforcement learning for self-adjusting process control. These directions aim to enhance the development of adaptive, intelligent, and efficient freeze-drying systems.
Safira et al. (Sat,) studied this question.
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